Files Included :
1 Welcome to the Course.mp4 (34.34 MB)
2 What makes this course Unique.mp4 (88.48 MB)
1 Building Trustworthy AI & NVIDIA Tools.mp4 (52.85 MB)
2 Trustworthy AI - Exam Guide.mp4 (1.01 GB)
1 Exam Tips & Instructions - watch this completely.mp4 (195.86 MB)
1 Introduction to Machine Learning Fundamentals.mp4 (45.35 MB)
10 Decision Tree Algorithms.mp4 (50.82 MB)
11 Loss Functions of Decision Trees.mp4 (42.29 MB)
12 Decision Tree Algorithm Implementation.mp4 (114.08 MB)
13 Overfit Vs Underfit - Kfold Cross validation.mp4 (110.73 MB)
14 Hyperparameter Optimization Techniques.mp4 (223.94 MB)
15 KNN Algorithm.mp4 (60.33 MB)
16 SVM Algorithm.mp4 (138.55 MB)
17 Ensemble Learning - Voting Classifier.mp4 (94.17 MB)
18 Ensemble Learning - Bagging Classifier & Random Forest.mp4 (127.58 MB)
19 Ensemble Learning - Boosting Adabost and Gradient Boost.mp4 (131.39 MB)
2 Introduction to Machine Learning.mp4 (95.4 MB)
20 Emsemble Learning XGBoost.mp4 (66.41 MB)
21 Clustering - Kmeans.mp4 (156.38 MB)
22 Clustering - Hierarchial Clustering.mp4 (74.44 MB)
23 Clustering - DBScan.mp4 (40.46 MB)
24 Time Series Analysis.mp4 (117.66 MB)
25 ARIMA Hands On.mp4 (76.74 MB)
3 Types of Machine Learning.mp4 (19.38 MB)
4 Linear Regression & Evaluation Metrics for Regression.mp4 (134.41 MB)
5 Regularization and Assumptions of Linear Regression.mp4 (165.11 MB)
6 Logistic Regression.mp4 (60.21 MB)
7 Gradient Descent.mp4 (49.5 MB)
8 Logistic Regression Implementation and EDA.mp4 (127.89 MB)
9 Evaluation Metrics for Classification.mp4 (169.78 MB)
1 Deep Learning Fundaments - Introduction.mp4 (23.77 MB)
10 Introduction to Convolutional Neural Networks.mp4 (136.76 MB)
11 Implementation of CNN on CatDog Dataset.mp4 (106.63 MB)
12 Transfer Learning for Computer Vision.mp4 (128.68 MB)
13 Feed Forward Neural Network Challenges.mp4 (152.4 MB)
14 RNN & Types of Architecture.mp4 (146.11 MB)
15 LSTM Architecture.mp4 (70.9 MB)
16 Attention Mechanism.mp4 (128.01 MB)
17 Transfer Learning for Natural Language Data.mp4 (68.05 MB)
2 Introduction to Deep Learning.mp4 (108.26 MB)
3 Introduction to Tensorflow & Create first Neural Network.mp4 (120.82 MB)
4 Intuition of Deep Learning Training.mp4 (130.23 MB)
5 Activation Function.mp4 (51.54 MB)
6 Architecture of Neural Networks.mp4 (28.43 MB)
7 Deep Learning Model Training - Epochs - Batch Size.mp4 (21.07 MB)
8 Hyperparameter Tuning in Deep Learning.mp4 (49.69 MB)
9 Vanshing & Exploding Gradients - Initializations, Regularizations.mp4 (55.12 MB)
1 Introduction to NLP Section.mp4 (6.66 MB)
10 Word Embeddings.mp4 (57.78 MB)
11 CBoW and Skip gram - word embeddings.mp4 (65.6 MB)
2 Introduction to NLP and NLP Tasks.mp4 (41.37 MB)
3 Understanding NLP Pipeline.mp4 (33.84 MB)
4 Text Preprocessing Techniques - Tokenization.mp4 (53.85 MB)
5 Text Preprocessing - Pos Tagging, Stop words, Stemming & Lemmatization.mp4 (30.85 MB)
6 Feature Extraction - NLP.mp4 (14.17 MB)
7 One Hot Encoding Technique.mp4 (25.37 MB)
8 Bag of Words & Count Vectorizer.mp4 (41.85 MB)
9 TF IDF Score.mp4 (38.34 MB)
1 Introduction to Large Language Models.mp4 (42.15 MB)
10 Understanding Masked Multi Head Attention.mp4 (20.54 MB)
11 Masked Multi Head Attention - Deep Dive.mp4 (33.43 MB)
12 Encoder Decoder Architecture.mp4 (49.35 MB)
13 Customization of LLMs - Prompt Engineering.mp4 (51.95 MB)
14 Customization of LLMs - Prompt Learning - Prompt Tuning & p-tuning.mp4 (61.08 MB)
15 Difference between Prompt Tuning and p-tuning.mp4 (19.11 MB)
16 PEFT - Parameter Efficient Fine Tuning.mp4 (47.41 MB)
17 Training data for LLMs.mp4 (39.03 MB)
18 Pillars of LLM Training Data Quality, Diversity, and Ethics.mp4 (51.85 MB)
19 Data Cleaning for LLMs.mp4 (45.58 MB)
2 How Large Language Models (LLMs) are trained.mp4 (69.11 MB)
20 Biases in Large Language Models.mp4 (32.23 MB)
21 Loss Functions for LLMs.mp4 (34.29 MB)
3 Capabilities of LLMs.mp4 (41.24 MB)
4 Challenges of LLMs.mp4 (37.7 MB)
5 Introduction to Transformers - Attention is all you need.mp4 (56.96 MB)
6 Positional Encodings.mp4 (56.71 MB)
7 Positional Encodings - Deep Dive.mp4 (99.05 MB)
8 Self Attention & Multi Head Attention.mp4 (32.92 MB)
9 Self Attention & Multi Head Attention - Deep Dive.mp4 (55.6 MB)
1 What is Prompt Engineering.mp4 (43.16 MB)
2 Advanced Prompt Engineering.mp4 (12.63 MB)
3 Techniques for Effective Prompts.mp4 (26.54 MB)
4 Ethical Considerations in Prompt Design for Large Language Models.mp4 (32.53 MB)
5 NVIDIA's Tools and Frameworks for Prompt Engineering.mp4 (27.07 MB)
6 NVIDIA Ecosystem tools for LLM Model Training.mp4 (24.56 MB)
1 Data Visualization & Analysis of LLMs.mp4 (33.57 MB)
2 EDA for LLMs.mp4 (24.67 MB)
1 Experiment Design Principles for LLMs.mp4 (33.28 MB)
2 Techniques for Large Language Models Experimentation.mp4 (29.18 MB)
3 Data Management and Version Control for LLM experimentation.mp4 (24.86 MB)
4 NVIDIA Ecosystem tools for LLM Experimentation, Data Management and Version Cont.mp4 (34.1 MB)
1 LLM Integration and Deployment.mp4 (50.66 MB)
2 Deployment Considerations for Large Language Models.mp4 (22.9 MB)
3 Monitoring and Maintenance of Large Language Models.mp4 (30.09 MB)
4 Explainability and Interpretability of Large Language Models.mp4 (42.34 MB)
5 NVIDIA Ecosystem Tools for Deployment and Integration.mp4 (59.19 MB)]
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